Satellite Images Of Hurricane Damage

Dataset Description

Overview

The data are satellite images from Texas after Hurricane Harvey divided into two groups (damage and no_damage). The goal is to make a model which can automatically identify if a given region is likely to contain flooding damage.

Dataset Distribution

Modeling: Keras Multi-layer Perceptron (MLP) for Image Classifications

A multi-layer perceptron (MLP) is a class of feedforward artificial neural network (ANN). The algorithm at each iteration uses the SparseCategoricalCrossentropy to measure the loss, and then the gradient and the model update is calculated. At the end of this iterative process, we would reach a better level of agreement between test and predicted sets since the error would be lower from that of the first step.

Compiling and fitting the model


References

  1. Kaggle Dataset: Satellite Images of Hurricane Damage
  2. Tensorflow API Documentation
  3. Quoc Dung Cao, Youngjun Choe, December 13, 2018, "Detecting Damaged Buildings on Post-Hurricane Satellite Imagery Based on Customized Convolutional Neural Networks", IEEE Dataport, doi: https://dx.doi.org/10.21227/sdad-1e56.